Hearing device with neural network-based microphone signal processing

ABSTRACT

A hearing system performs nonlinear processing of signals received from a plurality of microphones using a neural network to enhance a target signal in a noisy environment. In various embodiments, the neural network can be trained to improve a signal-to-noise ratio without causing substantial distortion of the target signal. An example of the target sound includes speech, and the neural network is used to improve speech intelligibility.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/662,931, filed Oct. 24, 2019, now issued as U.S. Pat. No. 10,993,051,which is a continuation of U.S. patent application Ser. No. 15/092,489,filed Apr. 6, 2016, now issued as U.S. Pat. No. 10,492,008, each ofwhich are incorporated by reference herein in their entirety.

TECHNICAL FIELD

This document relates generally to hearing systems and more particularlyto a system for processing microphone signals using a neural network.

BACKGROUND

Hearing devices provide sound for the wearer. Some examples of hearingdevices are headsets, hearing aids, speakers, cochlear implants, boneconduction devices, and personal listening devices. Hearing aids provideamplification to compensate for hearing loss by transmitting amplifiedsounds to their ear canals. Damage to outer hair cells in a patient'scochlea results in loss of frequency resolution in the patient'sauditory perception. As this condition develops, it becomes difficultfor the patient to distinguish target sound, such as speech, fromenvironmental noise. Simple amplification does not address suchdifficulty. Thus, there is a need to help such a patient in listening totarget sounds, such as speech, in a noisy environment.

SUMMARY

According to the present disclosure, a hearing system performs nonlinearprocessing of signals received from a plurality of microphones using aneural network to enhance a target signal in a noisy environment. Invarious embodiments, the neural network can be trained to improve asignal-to-noise ratio without causing substantial distortion of thetarget signal. An example of the target sound includes speech, and theneural network is used to improve speech intelligibility.

In an exemplary embodiment, a hearing system includes a plurality ofmicrophones, a control circuit, and a receiver (speaker). Themicrophones receive input sounds including a target sound and produce aplurality microphone signals including the target sound. The controlcircuit produces an output signal using the plurality of microphonesignals. The control circuit includes a neural network and controls adirectionality of the plurality of microphones by processing theplurality of microphone signals using a nonlinear signal processingalgorithm that is based on the neural network. The receiver produces anoutput sound using the output signal.

In an exemplary embodiment, a hearing system includes a pair of left andright hearing aids configured to be worn by a wear and communicativelycoupled to each other. The left and right hearing aids each include amicrophone, a control circuit, and a receiver. The microphone receivesinput sounds including a target sound and produces a microphone signalincluding the target sound. The control circuit produces an outputsignal using the microphone signals produced by microphones of the leftand right hearing aids. The control circuit includes a neural networkand controls a directionality of the microphones of the left and righthearing aids using a nonlinear signal processing algorithm that is basedon the neural network. The receiver produces an output sound using theoutput signal.

In an exemplary embodiment, a method for operating a hearing system toenhance a target sound is provided. Microphone signals including atarget sound are received from a plurality of microphones of the hearingsystem. The microphone signals are processed, using a neuralnetwork-based non-linear signal processing algorithm, to control adirectionality of the plurality of microphones and produce an outputsignal. An output sound is produced based on the output signal using areceiver of the hearing system.

This summary is an overview of some of the teachings of the presentapplication and not intended to be an exclusive or exhaustive treatmentof the present subject matter. Further details about the present subjectmatter are found in the detailed description and appended claims. Thescope of the present invention is defined by the appended claims andtheir legal equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary embodiment of ahearing system using a neural network for processing microphone signals.

FIG. 2 is a block diagram illustrating an exemplary embodiment of a pairof hearing aids including the neural network.

FIG. 3 is an illustration of an exemplary embodiment of a neuralnetwork.

FIG. 4 is a block diagram illustrating an exemplary embodiment of theneural network of FIG. 1 .

FIG. 5 is a graph illustrating performance of the exemplary neuralnetwork of FIG. 4 compared to performance of an exemplary ideal binauralbeamformer.

FIG. 6 is a graph illustrating performance of the exemplary neuralnetwork of FIG. 4 compared to performance of an exemplary ideal binauralbeamformer.

FIG. 7 is a block diagram illustrating another exemplary embodiment ofthe neural network of FIG. 1 .

FIG. 8 is a graph illustrating performance of the exemplary neuralnetwork of FIG. 7 compared to performance of an exemplary ideal binauralbeamformer.

FIG. 9 is a graph illustrating performance of the exemplary neuralnetwork of FIG. 7 compared to performance of an exemplary ideal binauralbeamformer.

FIG. 10 is a flow chart illustrating an exemplary embodiment of a methodfor processing microphone signals using a neural network in a hearingsystem.

DETAILED DESCRIPTION

The following detailed description of the present subject matter refersto subject matter in the accompanying drawings which show, by way ofillustration, specific aspects and embodiments in which the presentsubject matter may be practiced. These embodiments are described insufficient detail to enable those skilled in the art to practice thepresent subject matter. References to “an”, “one”, or “various”embodiments in this disclosure are not necessarily to the sameembodiment, and such references contemplate more than one embodiment.The following detailed description is demonstrative and not to be takenin a limiting sense. The scope of the present subject matter is definedby the appended claims, along with the full scope of legal equivalentsto which such claims are entitled.

This document discusses, among other things, a hearing system thatperforms neural network based processing of microphone signals toenhance target sounds for better listening, such as improving speechintelligibility in a noisy environment. Though speech intelligibility isdiscussed as a specific example in this document, the present subjectmatter can be applied in various hearing devices for enhancing targetsounds of any type (e.g., speech or music) in a noisy signal (e.g.,babble noise or machine noise). Such devices include, among otherthings, hearing assistance devices, such as headsets, hearing aids,speakers, cochlear implants, bone conduction devices, and personallistening devices.

Bilateral directional microphones and binaural beamforming have beenused in hearing assistance devices for processing signals includingspeeches with noisy background, with limited improvement insignal-to-noise ratio (SNR). The present subject matter can use a neuralnetwork based binaural algorithm that can achieve performance exceedingthe theoretical upper limit provided by a directional microphone or abinaural beamformer in processing microphone signals for a hearingassistance system. The neural network based binaural algorithm is anonlinear signal processing algorithm that can exceed the theoreticallimit achievable by the existing linear algorithms in processingbinaural microphone signals. Training of this neural network is highlyflexible and may take into account various measures as cost functions.Specific neural network structure and training strategy have beendesigned and tested to achieve a desirable balance between sound qualityand SNR improvement. In various embodiments, the neural network basednonlinear signal processing algorithm can introduce controllednonlinearity to the signals such that the SNR can be greatly improvedwhile the sound quality is not substantially compromised.

FIG. 1 is a block diagram illustrating an exemplary embodiment of ahearing system 100 that uses a neural network for processing microphonesignals. System 100 includes a plurality of microphones 1-N (102-1 to102-N), a control circuit 104, and a receiver (speaker) 106.

Microphones 102 produce a plurality of microphone signals includingspeech. In one embodiment, microphones 102 are two microphones (N=2). Invarious embodiments, microphones 102 can include two or moremicrophones. Microphones 102 are each communicatively coupled to controlcircuit 104 via a wired or wireless link. Control circuit 104 processesthe plurality of microphone signals to produce an output signal.Receiver 106 produces an output sound using the output signal andtransmits the output sound to a listener.

Control circuit 104 can include a neural network 108 and controldirectionality of microphones 102 using the plurality of microphonesignals by executing a neural network based signal processing algorithm.In various embodiments, the neural network based signal processingalgorithm can include a nonlinear signal processing algorithm. Invarious embodiments, neural network 108 can be trained to control thedirectionality of microphones 102 by processing the plurality ofmicrophone signals to achieve a desirable balance between the SNR (withthe clean speech being the target signal) and the distortion of thespeech, as further discussed below, with reference to FIGS. 3-9 . Invarious embodiments, control circuit 104 can precondition the pluralityof microphone signals before processing it using neural network 108,such as by amplifying and/or filtering each of the microphone signals asneeded. In various embodiments, control circuit 104 can process theoutput of neural network 108 to produce right output signal as needed.

In various embodiments, system 100 can be implemented entirely orpartially in hearing aids. For example, microphones 102 can include oneor more microphones in the hearing aids, one or more ad-hoc microphonearrays, and one or more remote microphones that are external to butcommunicatively coupled to the hearing aids. Control circuit 104 can beimplemented in one or more processors of the hearing aids and/or one ormore processors in an external device communicatively coupled to thehearing aids. One example of such an external device includes acellphone installed with an application implementing portions of controlcircuit 104. In addition to or in place of receiver 106 for transmittingthe output to the listener being a hearing aid wearer, the output can bedelivered to another person or device as needed, such as a user otherthan the hearing aid wearer or a speech recognizer.

FIG. 2 is a block diagram illustrating an exemplary embodiment of ahearing system 200, which represents an exemplary embodiment of system100 with a pair of hearing aids each including a neural network. System200 includes a left hearing aid 210L and a right hearing aid 210Rcommunicatively coupled to each other via a wireless binaural link 216.

Left hearing aid 210L can be configured to be worn in or about the leftear of a hearing aid wearer and includes a hearing aid circuit 212L anda shell 214L that houses hearing aid circuit 212L. Examples of shell214L include, but are not limited to, a housing for a BTE, ITE, ITC,RIC, CIC, RITE or deep insertion types of hearing aids for use with theleft ear. Hearing aid circuit 212L includes a microphone 202L, anantenna 220L, a communication circuit 218L, a control circuit 204L, anda receiver (speaker) 206L. Microphone 202L receives sounds from theenvironment of the hearing aid wearer and produces a left microphonesignal representing the received sounds. Communication circuit 218Lperforms wireless communication including ear-to-ear communication withright hearing aid 210R using antenna 220L via binaural link 216. Controlcircuit 204L processes the left microphone signal and a right microphonesignal received by communication circuit 218L to produce a left outputsignal. Receiver 206L produces a left sound using the left output signaland transmits the left sound to the left ear of the hearing aid wearer.

Right hearing aid 210R can be configured to be worn in or about theright ear of the hearing aid wearer and includes a hearing aid circuit212R and a shell 214R that houses hearing aid circuit 212R. Examples ofshell 214R include, but are not limited to, housing for a BTE, ITE, ITC,RIC, CIC, RITE or deep insertion types of hearing aids for use with theright ear. Hearing aid circuit 212R includes a microphone 202R, anantenna 220R, a communication circuit 218R, a control circuit 204R, anda receiver (speaker) 206R. Microphone 202R receives sounds from theenvironment of the wearer and produces a right microphone signalrepresenting the received sounds. Communication circuit 218R performswireless communication including ear-to-ear communication with lefthearing aid 210L using antenna 220R via binaural link 216. Controlcircuit 204R processes the right microphone signal and the leftmicrophone signal received by communication circuit 218R to produce aright output signal. Receiver 206L produces a right sound using theright output signal and transmits the right sound to the left ear of thehearing aid user.

Control circuit 204L represents an exemplary embodiment of controlcircuit 104 and includes a neural network 208L. Control circuit 204Ralso represents an exemplary embodiment of control circuit 104 andincludes a neural network 208R. Examples of neural networks 208L and208R include neural network 108 including its various embodiments asdiscussed in this document. In various embodiments, control circuit 204Lcan precondition the left microphone signal before processing it usingneural network 208L and/or processes the output of neural network 208Lto produce the left output signal as needed. Control circuit 204Rpreconditions the right microphone signal before processing it usingneural network 208R and/or processes the output of neural network 208Rto produce the right output signal, as needed.

FIG. 3 is an illustration of an exemplary embodiment of a neural network308. The illustrated embodiment is an example of a time domain neuralnetwork structure that takes delayed time domain signals (x(t), x(t−1),x(t−2), x(t−3), . . . ) as inputs and generate y(t) as output. This typeof structure can be easily modified to process multiple input signalsand generate multiple output signals. Neural network 308 can be trainedto process microphone signals such as the signals output frommicrophones 102 or microphones 202L and 202R, and provide an outputsignal with improved SNR for improved speech intelligibility. In anexemplary embodiment, neural network 308 is a fixed neural network thatremains unchanged after training. In another exemplary embodiment,neural network 308 is an adaptive neural network that is adaptive to achanging environment in which the hearing system is used. In variousembodiments, neural network 308 can be a time domain neural network(such as illustrated in FIG. 3 ) or a frequency domain neural network.The structure of neural network 308 can be highly flexible.

FIG. 4 is a block diagram illustrating an exemplary embodiment of aneural network 408, which represent an exemplary embodiment of neuralnetwork 108. Neural network 408 includes an input 424, a nonlinearhidden layer 426, a linear output layer 428, and an output 430. In theillustrated embodiment, input 424 receives time sequence samples frommicrophones (such as from binaural microphones 202L and 202R, with 32samples from each microphone). In various embodiments, neural network408 can be trained on synthesized target sound (e.g., speech or music)in various types of noise conditions (e.g., with babble noise or machinenoise) using various cost functions. Examples of the cost functionsinclude mean squared error (MSE), weighted MSE, mean absolute error(MAE), statistical forecast error (SFE), and perceptual inspired metricssuch as SII (speech intelligibility index) and STI (speech transmissionindex). In one experiment, for example, neural network 408 was trainedon synthesized speech in babble noise conditions with the desired speechcoming from front. During the training, the target signal was the cleanspeech and the MSE, a cost function, was minimized by properly adjustingsynaptic weights in neural network 408, which included a plurality ofsynapses. After the training, the performance of neural network 408 inSNR improvement was tested on a separate training dataset and wascompared to an ideal binaural beamformer (a linear binaural beamformeroptimized for the testing condition). FIGS. 5 and 6 are each a graphillustrating performance of neural network 408 (NN OUTPUT) compared toperformance of the ideal binaural beamformer (BBF). The graph plots anSNR 434 of neural network 408 and an SNR 436 of the ideal BBF over arange of frequencies, and shows the SNR improvement achieved by neuralnetwork 408. FIG. 5 shows the SNR improvement on an input signal havingan average SNR of 5 dB. FIG. 6 shows the SNR improvement on an inputsignal having an average SNR of 0 dB.

The fact that neural network 408 can improve the SNR to an extent thatexceeds the theoretical limit of linear binaural beamformer indicatesthat neural network 408 introduces nonlinearity to the signal. However,though a good SNR improvement was achieved, the distortion to thedesired speech as well as the noise could be annoying. To reduce theaudible distortion, sound quality measures can be incorporated into thecost function, the structure of the neural network can be adjusted,and/or the training data can be adjusted. The following is an exampledemonstrating a specific network structure (illustrated in FIG. 7 )combined with carefully designed training data to achieve a balancebetween SNR improvement and distortion.

FIG. 7 is a block diagram illustrating an exemplary embodiment of aneural network 708, which represents another exemplary embodiment ofneural network 108. Neural network 708 includes an input 724, a linearfirst hidden layer 726, a nonlinear second hidden layer 727, a linearoutput layer 728, and an output 730. In the illustrated embodiment,input 724 receives time sequence samples from microphones (such as frombinaural microphones 202L and 202R, with 16 samples from eachmicrophone). A shortcut connection 732 between the output of the linearfirst hidden layer 726 and the input of output layer 728 provides adirect path for a portion of the input signal to pass through withoutnonlinear distortion. Thus, neural network 708 includes a linear signalprocessing path between input 724 and output 730 and a nonlinear signalprocessing path between input 724 and output 730. The linear pathincludes first hidden layer 726 and output layer 728. The nonlinear pathincludes first hidden layer 726, second hidden layer 727, and outputlayer 728. In other words, first hidden layer 726 has an input directlyconnected to input 724 and an output. Second hidden layer 727 has aninput directly connected to the output of hidden layer 726 and anoutput. Output layer 728 has an input directly connected to the outputof hidden layer 726, another input directly connected to the output ofhidden layer 727, and an output directly connected to output 730.

Neural network 708 was trained at SNRs of 0 dB, 10 dB, and 20 dB withthe target signal always being the clean speech. The training is also acrucial step for reducing distortion of the speech. FIGS. 8 and 9 areeach a graph illustrating performance of neural network 708 (NN OUTPUT)compared to performance of the ideal binaural beamformer (BBF). Thegraph plots an SNR 734 of neural network 708 and SNR 436 of the idealBBF over a range of frequencies, and shows the SNR improvement achievedby neural network 708. FIG. 8 shows the SNR improvement on an inputsignal having an average SNR of 5 dB. FIG. 9 shows the SNR improvementon an input signal having an average SNR of 0 dB. Compared to theexample of neural network 408 as discussed above with reference to FIGS.5-7 , neural network 708 provided less SNR improvement (though stillhigher than the ideal BBF), but the distortion associated with neuralnetwork was virtually unperceivable.

Neural network 708 is illustrated in FIG. 7 and discussed above by wayof example, but not by way of restriction. In various embodiments,neural network 108 can each include a linear signal processing path anda nonlinear signal processing path such that the output includescomponents being the input subjected to only linear signal processingand therefore not distorted as a result of nonlinear processing. Forexample, the nonlinear signal processing path can include one or morelinear layers and one or more nonlinear layers, and the liner signalprocessing path can include only the one or more linear layers whilebypassing each of the one or more nonlinear layers.

In various embodiments, the cost function in the training of neuralnetwork 108, including its various embodiments, can incorporate variousspeech intelligibility and sound quality measures to optimize the neuralnetwork for various working conditions and/or user preferences. Invarious embodiments, neural network 108, including its variousembodiments, can be trained in both time domain and frequency domain. Invarious embodiments, neural network 108, including its variousembodiments, can be fixed (i.e., kept unchanged after the training) oradaptive (i.e., dynamically adjustable based on the real environment).In various embodiments, neural network 108, including its variousembodiments, can be implemented digitally, in the form of analogcircuits, or as a combination of digital and analog circuits.

FIG. 10 is a flow chart illustrating an exemplary embodiment of a method1040 for processing microphone signals using a neural network in ahearing system. Examples of the hearing system include system 100 andits various embodiments as described by this document. Examples for theneural network used in performing method 1040 include neural network 108and its various embodiments as discussed in this document. In variousembodiments, method 1040 can be performed to enhance a target sound in anoisy background for better listening to the target sound. In anexemplary embodiment, as discussed below as an example, the target soundis a speech, and method 1040 can be performed to improve intelligibilityof speech in a noisy background.

At 1042, microphone signals are received from a plurality of microphonesof the hearing system. The microphone signals include a speech receivedby the microphones. In an exemplary embodiment, the hearing systemincludes a pair of left and right hearing aids each being worn in orabout an ear of a hearing aid wearer, such as the pair of left and righthearing aids 210L and 210R. The received microphone signals include aleft microphone signal received from the left hearing aid and a rightmicrophone signal received from the right hearing aid.

At 1044, the microphone signals are processed, using a neuralnetwork-based signal processing algorithm, to control a directionalityof the plurality of microphones and produce an output signal. In variousembodiments, the neural network-based signal processing algorithm caninclude a nonlinear signal processing algorithm. This includes, forexample, processing the microphone signals using a linear signalprocessing path and a nonlinear signal processing path. In variousembodiments, the microphone signals can be processed using a neuralnetwork trained for a desirable balance between an SNR and distortion ofthe speech. In an exemplary embodiment, the neural network is trainedwith a clean speech as the target signal and a mean squared error as acost function. In an exemplary embodiment, the mean squared error isapproximately minimized by adjusting synaptic weights in the neuralnetwork. In various embodiments, the microphone signals can be processedwithin a hearing device, such as a hearing aid, and/or one or moredevices external to but communicatively coupled to the hearing aid. Anexample of such an external device include a cellphone. This allows fora distributed processing that off-loads the processing work from thehearing aid.

At 1046, an output sound is produced based on the output signal using areceiver (speaker) of the hearing assistance system. The output sound isdelivered to the user of the hearing assistance system, such as ahearing aid wearer when the hearing assistance system includes the pairof left and right hearing aids.

In various embodiments, the present subject matter provides a neuralnetwork based binaural algorithm that can achieve performance exceedingthe theoretical upper limit provided by a binaural beamformer inprocessing microphone signals. Neural network 408, as discussed abovewith reference to FIGS. 4-6 , is an example that demonstrates that asubstantially better SNR improvement can be achieved when compared tothe upper limit of SNR improvement provided by a binaural beamformer.However, this is achieved at the cost of obvious nonlinear distortionsto the target signal as well as the noise. If the distortion to thetarget signal is of concern, one could incorporate sound qualitymeasures into the cost function during training of the neural network,adjust the structure of the neural network, and/or adjust training data.Neural network 708, as discussed above with reference to FIGS. 7-9 , isan example that demonstrates a specific neural network structure thatcan achieve a balance between SNR improvement and distortion of thetarget signal when combined with carefully designed training data.

Hearing devices typically include at least one enclosure or housing, amicrophone, hearing device electronics including processing electronics,and a speaker or “receiver.” Hearing devices may include a power source,such as a battery. In various embodiments, the battery may berechargeable. In various embodiments multiple energy sources may beemployed. It is understood that in various embodiments the microphone isoptional. It is understood that in various embodiments the receiver isoptional. It is understood that variations in communications protocols,antenna configurations, and combinations of components may be employedwithout departing from the scope of the present subject matter. Antennaconfigurations may vary and may be included within an enclosure for theelectronics or be external to an enclosure for the electronics. Thus,the examples set forth herein are intended to be demonstrative and not alimiting or exhaustive depiction of variations.

It is understood that digital hearing aids include a processor. Forexample, control circuit 104 and its various embodiments may beimplemented in a processor. In digital hearing aids with a processor,programmable gains may be employed to adjust the hearing aid output to awearer's particular hearing impairment. The processor may be a digitalsignal processor (DSP), microprocessor, microcontroller, other digitallogic, or combinations thereof. The processing may be done by a singleprocessor, or may be distributed over different devices. The processingof signals referenced in this application can be performed using theprocessor or over different devices. Processing may be done in thedigital domain, the analog domain, or combinations thereof. Processingmay be done using subband processing techniques. Processing may be doneusing frequency domain or time domain approaches. Some processing mayinvolve both frequency and time domain aspects. For brevity, in someexamples drawings may omit certain blocks that perform frequencysynthesis, frequency analysis, analog-to-digital conversion,digital-to-analog conversion, amplification, buffering, and certaintypes of filtering and processing. In various embodiments the processorcan be adapted to perform instructions stored in one or more memories,which may or may not be explicitly shown. Various types of memory may beused, including volatile and nonvolatile forms of memory. In variousembodiments, the processor or other processing devices can executeinstructions to perform a number of signal processing tasks. Suchembodiments may include analog components in communication with theprocessor to perform signal processing tasks, such as sound reception bya microphone, or playing of sound using a receiver (i.e., inapplications where such transducers are used). In various embodiments,different realizations of the block diagrams, circuits, and processesset forth herein can be created by one of skill in the art withoutdeparting from the scope of the present subject matter.

Various embodiments of the present subject matter support wirelesscommunications with a hearing device. In various embodiments thewireless communications can include standard or nonstandardcommunications. Some examples of standard wireless communicationsinclude, but not limited to, Bluetooth™, low energy Bluetooth, IEEE802.11(wireless LANs), 802.15 (WPANs), and 802.16 (WiMAX). Cellularcommunications may include, but not limited to, CDMA, GSM, ZigBee, andultra-wideband (UWB) technologies. In various embodiments, thecommunications are radio frequency communications. In variousembodiments the communications are optical communications, such asinfrared communications. In various embodiments, the communications areinductive communications. In various embodiments, the communications areultrasound communications. Although embodiments of the present systemmay be demonstrated as radio communication systems, it is possible thatother forms of wireless communications can be used. It is understoodthat past and present standards can be used. It is also contemplatedthat future versions of these standards and new future standards may beemployed without departing from the scope of the present subject matter.

The wireless communications support a connection from other devices.Such connections include, but are not limited to, one or more mono orstereo connections or digital connections having link protocolsincluding, but not limited to 802.3 (Ethernet), 802.4, 802.5, USB, ATM,Fibre-channel, Firewire or 1394, InfiniBand, or a native streaminginterface. In various embodiments, such connections include all past andpresent link protocols. It is also contemplated that future versions ofthese protocols and new protocols may be employed without departing fromthe scope of the present subject matter.

In various embodiments, the present subject matter is used in hearingdevices that are configured to communicate with mobile phones. In suchembodiments, the hearing device may be operable to perform one or moreof the following: answer incoming calls, hang up on calls, and/orprovide two way telephone communications. In various embodiments, thepresent subject matter is used in hearing devices configured tocommunicate with packet-based devices. In various embodiments, thepresent subject matter includes hearing devices configured tocommunicate with streaming audio devices. In various embodiments, thepresent subject matter includes hearing devices configured tocommunicate with Wi-Fi devices. In various embodiments, the presentsubject matter includes hearing devices capable of being controlled byremote control devices.

It is further understood that different hearing devices may embody thepresent subject matter without departing from the scope of the presentdisclosure. The devices depicted in the figures are intended todemonstrate the subject matter, but not necessarily in a limited,exhaustive, or exclusive sense. It is also understood that the presentsubject matter can be used with a device designed for use in the rightear or the left ear, or both ears, of the wearer.

The present subject matter may be employed in hearing devices, such asheadsets, hearing aids, speakers, cochlear implants, bone conductiondevices, and personal listening devices.

The present subject matter is demonstrated for use in hearing devices,such as hearing aids, including but not limited to, behind-the-ear(BTE), in-the-ear (ITE), in-the-canal (ITC), receiver-in-canal (RIC), orcompletely-in-the-canal (CIC) type hearing aids. It is understood thatbehind-the-ear type hearing aids may include devices that residesubstantially behind the ear or over the ear. Such devices may includehearing aids with receivers associated with the electronics portion ofthe behind-the-ear device, or hearing aids of the type having receiversin the ear canal of the user, including but not limited toreceiver-in-canal (RIC) or receiver-in-the-ear (RITE) designs. Thepresent subject matter can also be used in hearing assistance devicesgenerally, such as cochlear implant type hearing devices. The presentsubject matter can also be used in deep insertion devices having atransducer, such as a receiver or microphone. The present subject mattercan be used in devices whether such devices are standard or custom fitand whether they provide an open or an occlusive design. It isunderstood that other hearing devices not expressly stated herein may beused in conjunction with the present subject matter.

This application is intended to cover adaptations or variations of thepresent subject matter. It is to be understood that the abovedescription is intended to be illustrative, and not restrictive. Thescope of the present subject matter should be determined with referenceto the appended claims, along with the full scope of legal equivalentsto which such claims are entitled.

What is claimed is:
 1. A system for transmitting an output sound to alistener using heating devices configured to be worn by the listener,the hearing devices configured to produce the output sound using anoutput signal and including microphones configured to receive inputsounds and to produce microphone signals using the input sounds, thesystem comprising: an external device external to the hearing devicesand configured to be communicatively coupled to the hearing devices, theexternal device including a control circuit configured to produce theoutput signal using a processed signal, the control circuit including aneural network configured to receive the microphone signals and tocontrol directionality of the microphones by processing the microphonesignals using a nonlinear signal processing algorithm to produce theprocessed signal.
 2. The system of claim 1, wherein the neural networkcomprises a nonlinear signal processing path and a linear signalprocessing path.
 3. The system of claim 2, wherein the nonlinear signalprocessing path comprises a nonlinear hidden layer, and the linearsignal processing path is formed by a shortcut connection bypassing thenonlinear hidden layer.
 4. The system of claim 2, wherein the linearsignal processing path comprises a linear hidden layer configured toreceive the microphone signals and to produce a linear output signal,the nonlinear signal processing path comprises a nonlinear hidden layerconfigured to receive the linear output signal and to produce anonlinear output signal, and the neural network further comprises anoutput layer configured to produce the processed signal using the linearoutput signal and the nonlinear output signal.
 5. The system of claim 1,further comprising one or more remote microphones external to thehearing devices and configured to produce one or more remote microphonesignals, and wherein the neural network is configured to receive themicrophone signals and the one or more remote microphone signals and tocontrol the directionality of the microphones by processing themicrophone signals and the one or more remote microphone signals usingthe nonlinear signal processing algorithm to produce the processedsignal.
 6. The system of claim 1, wherein the target sound includesspeech, and the neural network is configured to improve speechintelligibility.
 7. The system of claim 6, wherein the neural network isconfigured to be adaptive to a changing environment in which the hearingdevices are used.
 8. A system or transmitting an output sound to alistener, the system comprising: hearing devices configured to be wornby the listener and configured to produce an output sound using anoutput signal, the hearing devices including microphones configured toreceive input sounds and to produce microphone signals using the inputsounds; and a control circuit configured to produce the output signalusing a processed signal and including a neural network configured toreceive the microphone signals and to control directionality of themicrophones by processing the microphone signals using a nonlinearsignal processing algorithm to produce the processed signal, the neuralnetwork including: a nonlinear signal processing path including anonlinear hidden layer; and a linear signal processing path formed by ashortcut connection bypassing the nonlinear hidden layer.
 9. The systemof claim 8, comprising an external device external to the hearingdevices and configured to be communicatively coupled to the hearingdevices, the external device including the control circuit.
 10. Thesystem of claim 9, further comprising one or more remote microphonesexternal to the hearing devices and configured to produce one or moreremote microphone signals, and wherein the neural network is configuredto receive the microphone signals and the one or more remote microphonesignals and to control the directionality of the microphones byprocessing the microphone signals and the one or more remote microphonesignals using the nonlinear signal processing algorithm to produce theprocessed signal.
 11. A method for transmitting an output sound to alistener, the method comprising: receiving microphone signals producedby microphones in hearing devices worn by the listener, the microphonesignals representing an input sound received by the microphones;processing the microphone signals to control directionality of themicrophones using a neural network of a control circuit of an externaldevice, the processing including processing the microphone signals usinga nonlinear signal processing algorithm to produce a processed signal,the external device external to the hearing devices and configured to becommunicatively coupled to the hearing devices; producing an outputsignal based on the processed signal using the control circuit; andproducing an output sound based on the output signal using the hearingdevices.
 12. The method of claim 11, wherein processing the microphonesignals comprises processing the microphone signals through a nonlinearsignal processing path and a linear signal processing path of the neuralnetwork, the nonlinear signal processing path including a nonlinearhidden layer, the linear signal processing path formed by a shortcutconnection bypassing the nonlinear hidden layer.
 13. The method of claim11, further comprising: receiving one or more remote microphone signalsfrom one or more remote microphones external to the hearing devices; andprocessing the microphone signals and the one or more remote microphonesignals to control the directionality of the microphones using theneural network of the control circuit.
 14. The method of claim 11,wherein processing the microphone signals comprises processing themicrophone signals to improve speech intelligibility.
 15. The method ofclaim 14, further comprising training the neural network to process themicrophone signals for a desirable balance between a signal-to-noiseratio (SNR) and distortion of a target sound included in the inputsound.
 16. The method of claim 15, wherein training the neural networkcomprises training the neural network using a synthesized target soundin a noisy condition and a specified cost function.
 17. The method ofclaim 16, wherein training the neural network comprises training theneural network using a clean speech in a babble noise condition as thesynthesized target sound in the noisy condition, and a mean squarederror as the cost function.
 18. The system of claim 1, wherein theexternal device is a cellphone.
 19. The system of claim 6, wherein theneural network is trained to process the microphone signals for adesirable balance between a signal-to-noise ratio (SNR) and distortionof the speech.
 20. The method of claim 11, wherein using the neuralnetwork of the control circuit of the external device comprises using acellphone installed with an application implementing portions of thecontrol circuit including the neural network.